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Learning multiobjective rough terrain traversability
Umeå University, Faculty of Science and Technology, Department of Physics. (Digital Physics)
Umeå University, Faculty of Science and Technology, Department of Physics. (Digital Physics)
Umeå University, Faculty of Science and Technology, Department of Physics. (Digital Physics)
Swedish University of Agricultural Sciences, Sweden.ORCID iD: 0000-0002-7112-8015
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2022 (English)In: Journal of terramechanics, ISSN 0022-4898, E-ISSN 1879-1204, Vol. 102, p. 17-26Article in journal (Refereed) Published
Abstract [en]

We present a method that uses high-resolution topography data of rough terrain, and ground vehicle simulation, to predict traversability. Traversability is expressed as three independent measures: the ability to traverse the terrain at a target speed, energy consumption, and acceleration. The measures are continuous and reflect different objectives for planning that go beyond binary classification. A deep neural network is trained to predict the traversability measures from the local heightmap and target speed. To produce training data, we use an articulated vehicle with wheeled bogie suspensions and procedurally generated terrains. We evaluate the model on laser-scanned forest terrains, previously unseen by the model. The model predicts traversability with an accuracy of 90%. Predictions rely on features from the high-dimensional terrain data that surpass local roughness and slope relative to the heading. Correlations show that the three traversability measures are complementary to each other. With an inference speed 3000 times faster than the ground truth simulation and trivially parallelizable, the model is well suited for traversability analysis and optimal path planning over large areas.

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 102, p. 17-26
Keywords [en]
Traversability, Rough terrain vehicle, Multibody simulation, Laser scan, Deep learning
National Category
Robotics
Research subject
Physics; Computerized Image Analysis; data science; Physics
Identifiers
URN: urn:nbn:se:umu:diva-193680DOI: 10.1016/j.jterra.2022.04.002ISI: 000805039700001Scopus ID: 2-s2.0-85129503568OAI: oai:DiVA.org:umu-193680DiVA, id: diva2:1651276
Projects
Mistra Digital Forest
Funder
Mistra - The Swedish Foundation for Strategic Environmental Research, 2017/14 #6eSSENCE - An eScience CollaborationSwedish National Infrastructure for Computing (SNIC), 2021/5-234Available from: 2022-04-11 Created: 2022-04-11 Last updated: 2023-05-08Bibliographically approved
In thesis
1. Terrain machine learning
Open this publication in new window or tab >>Terrain machine learning
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Maskininlärning i terräng
Abstract [en]

The use of heavy vehicles in rough terrain is vital in the industry but has negative implications for the climate and ecosystem. In addition, the demand for improved efficiency underscores the need to enhance these vehicles' navigation capabilities. Navigating rough terrain presents distinct challenges, including deformable soil, surface roughness, and spatial and temporal terrain variability. Focusing on forestry, this thesis aims to improve navigation using machine learning and physics simulations. Without considering the vehicle-terrain dynamics, methods for navigation can result in unsafe or unnecessarily challenging situations. Specifically, we address route planning, control for autonomous vehicles, and soilde formations. We simulate soil using the discrete element method and vehicles using multibody dynamics.

To enhance route planning, we train a predictor model that uses a height map of the terrain to predict measures of traversability. The model has a directional dependency, couples geometric terrain features with vehicle design and dynamics, and allows for swift evaluations over large areas. The proposed method facilitates detailed route planning, using multiple objectives to yield efficient solutions.

We address autonomy in rough terrain navigation by training a controller through deep reinforcement learning. The control policy uses a local height map for perception to plan and control a forwarder with actively articulated suspensions. The controller adapts to overcome various obstacles and demonstrates skilled driving in rough terrain.

Extending beyond simulation, we address the simulation-to-reality gap of vehicles with complex hydraulic drivelines through system identification and domain randomization. The results show that having an accurate model of the actuators, modelling system delays, and preventing bang-bang control yields successful transfer. Controllers that train in simulation and transfer to reality are a step toward autonomous vehicles.

While the previously mentioned studies assume rigid terrain, we also answer if the discrete element method can capture large soil deformations due to heavy traffic. The results show that the discrete element method can represent a wide variety of natural soil and that the resulting rut depths agree well with empirical models and experimental data, including multipass scenarios.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2023. p. 38
Keywords
multibody dynamics simulation, rough terrain vehicle, autonomous vehicles, robotics control, discrete element method, sim-to-real, reinforcement learning
National Category
Robotics Other Physics Topics Forest Science Computer Vision and Robotics (Autonomous Systems)
Research subject
Physics
Identifiers
urn:nbn:se:umu:diva-207982 (URN)978-91-8070-060-3 (ISBN)978-91-8070-059-7 (ISBN)
Public defence
2023-06-01, NAT.D.410, Umeå, 09:00 (English)
Opponent
Supervisors
Funder
Mistra - The Swedish Foundation for Strategic Environmental Research, DIA 2017/14 #6
Available from: 2023-05-11 Created: 2023-05-05 Last updated: 2023-05-08Bibliographically approved

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fulltext(3997 kB)123 downloads
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Wallin, ErikWiberg, ViktorVesterlund, FolkeServin, Martin

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Citation style
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